def test_construction():
# Transition matrix
transition_matrix = np.array([[0.5, 0.5, 0., 0., 0.],
[0.25, 0.5, 0.25, 0., 0.],
[0., 0.25, 0.5, 0.25, 0.],
[0., 0., 0.25, 0.5, 0.25],
[0., 0., 0., 0.5, 0.5]])
states = np.arange(1, 6)
def trans_func(i, j):
if i - j == 0:
return 0.5
elif 2 <= i <= 4:
if abs(i - j) == 1:
return 0.25
else:
return 0
elif i == 1:
if j == 2:
return 0.5
else:
return 0
elif i == 5:
if j == 4:
return 0.5
else:
return 0
table = Table().states(states).transition_function(trans_func)
transition_prob = transition_matrix.reshape((25))
table2 = Table().states(states).transition_probability(transition_prob)
table_to_mc = table.to_markov_chain()
table2_to_mc = table2.to_markov_chain()
mc_from_table = MarkovChain.from_table(table)
mc_from_function = MarkovChain.from_transition_function(states, trans_func)
mc_from_matrix = MarkovChain.from_matrix(states, transition_matrix)
assert_array_equal(transition_matrix, table_to_mc.get_transition_matrix())
assert_array_equal(transition_matrix, table2_to_mc.get_transition_matrix())
assert_array_equal(transition_matrix,
mc_from_table.get_transition_matrix())
assert_array_equal(transition_matrix,
mc_from_function.get_transition_matrix())
assert_array_equal(transition_matrix,
mc_from_matrix.get_transition_matrix())
# Negative probability.
with pytest.warns(UserWarning):
MarkovChain.from_matrix([1, 2], [[-1, 2], [0.5, 0.5]])
# Transition probability doesn't sum to 1.
with pytest.warns(UserWarning):
MarkovChain.from_matrix([1, 2], [[0.2, 0.3], [1, 2]])
def test_construction():
# String values
table_str = Table().values('X', ['H', 'T'], 'Y', ['H', 'T'])
table_str = table_str.probabilities(np.array([0.24, 0.36, 0.16, 0.24]))
dist = table_str.to_joint()
assert_joint_equal(dist, np.array([[0.36, 0.24], [0.24, 0.16]]))
assert_equal(dist.get_possible_values(), [['H', 'T'], ['T', 'H']])
dist = table_str.to_joint(reverse=False)
assert_joint_equal(dist, np.array([[0.24, 0.16], [0.36, 0.24]]))
assert_equal(dist.get_possible_values(), [['H', 'T'], ['H', 'T']])
# Int values
table_int = Table().values('X', [1], 'Y', [2]).probabilities([1])
dist = JointDistribution.from_table(table_int)
x_values = dist.get_possible_values('X')
assert_approx_equal(x_values, [1])
assert isinstance(x_values[0], int)
y_values = dist.get_possible_values('Y')
assert_approx_equal(y_values, [2])
assert isinstance(y_values[0], int)
# Float values
table_float = Table().values('X', [1.1], 'Y', [2.2]).probabilities([1])
dist = JointDistribution.from_table(table_float)
x_values = dist.get_possible_values('X')
assert isinstance(x_values[0], float)
# Negative probability values
with pytest.warns(UserWarning):
Table().values('X', [1, 2], 'Y', [3])\
.probabilities([-0.5, 1.5]).to_joint()
# Doesn't sum to 1
with pytest.warns(UserWarning):
Table().values('X', [1, 2], 'Y', [3])\
.probabilities([1., 1.]).to_joint()
import pytest
from prob140 import (
JointDistribution,
Table,
)
from . import (
assert_approx_equal,
assert_dist_equal,
assert_equal,
assert_joint_equal,
)
DIST1 = Table().values([0, 1],
[2, 3, 4]).probabilities([0.1, 0.2, 0., 0.3, 0.4,
0.]).to_joint()
DIST2 = Table().values('Coin1', ['H', 'T'], 'Coin2',
['H', 'T']).probabilities([0.24, 0.36, 0.16,
0.24]).to_joint()
def test_construction():
# String values
table_str = Table().values('X', ['H', 'T'], 'Y', ['H', 'T'])
table_str = table_str.probabilities(np.array([0.24, 0.36, 0.16, 0.24]))
dist = table_str.to_joint()
assert_joint_equal(dist, np.array([[0.36, 0.24], [0.24, 0.16]]))
assert_equal(dist.get_possible_values(), [['H', 'T'], ['T', 'H']])
dist = table_str.to_joint(reverse=False)
assert_joint_equal(dist, np.array([[0.24, 0.16], [0.36, 0.24]]))
from numpy.testing import assert_array_equal
import pytest
from . import (
assert_approx_equal,
assert_dist_equal,
)
from prob140 import (
MarkovChain,
Table,
)
MC_SIMPLE = MarkovChain.from_matrix(states=np.array(['A', 'B']),
transition_matrix=np.array([[0.1, 0.9],
[0.8, 0.2]]))
START_SIMPLE = Table().states(['A', 'B']).probabilities([0.8, 0.2])
def test_construction():
# Transition matrix
transition_matrix = np.array([[0.5, 0.5, 0., 0., 0.],
[0.25, 0.5, 0.25, 0., 0.],
[0., 0.25, 0.5, 0.25, 0.],
[0., 0., 0.25, 0.5, 0.25],
[0., 0., 0., 0.5, 0.5]])
states = np.arange(1, 6)
def trans_func(i, j):
if i - j == 0:
return 0.5
from scipy import stats import numpy as np from prob140 import Table from prob140 import Plot # distribucion binomial ploteada n = 4 p = 3 / 4 k = np.arange(n + 1) binom_4_dist = stats.binom.pmf(k, n, p) binom_4_dist_graph = Table().values(k).probabilities(binom_4_dist) Plot(binom_4_dist_graph)
def test_normalized():
with pytest.warns(UserWarning):
dist = Table().values([1, 2, 3]).probabilities([1] * 3)
assert_dist_equal(dist.normalized(), [1 / 3] * 3)
def test_construction():
domain = Table().values(np.array([1, 2, 3]))
assert domain.num_columns == 1
dist1 = domain.probabilities(np.array([0.1, 0.2, 0.7]))
assert dist1.num_columns == 2
dist2 = domain.probability_function(lambda x: x / 6)
assert dist2.num_columns == 2
# Negative probability.
with pytest.warns(UserWarning):
domain.probabilities([0, 1.1, -0.1])
with pytest.warns(UserWarning):
domain.probability_function(lambda x: -x / 6)
# Probability doesn't sum to 1.
with pytest.warns(UserWarning):
domain.probabilities([0.3, 0.1, 0.2])
with pytest.warns(UserWarning):
domain.probability_function(lambda x: x)
def test_remove_zeros():
dist = Table().values([2, 3, 4, 5]).probabilities([0.5, 0.0, 0.5, 0])
assert dist.remove_zeros().num_rows == 2
import numpy as np
import pytest
from prob140 import (
emp_dist,
Table,
)
from . import (
assert_approx_equal,
assert_dist_equal,
)
SIMPLE_DIST = Table().values([1, 2, 3]).probabilities([0.2, 0.3, 0.5])
UNIFORM_DIST = Table().values(np.arange(100)).probabilities([0.01] * 100)
NEGATIVE_DIST = Table().values([-2, -1, 0, 1]).probabilities([0.25] * 4)
NONINTEGER_DIST = Table().values([-1.5, -0.5, 0.5,
1.5]).probabilities([0.25] * 4)
def test_construction():
domain = Table().values(np.array([1, 2, 3]))
assert domain.num_columns == 1
dist1 = domain.probabilities(np.array([0.1, 0.2, 0.7]))
assert dist1.num_columns == 2
dist2 = domain.probability_function(lambda x: x / 6)
assert dist2.num_columns == 2